Graph models [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]} are widely used in MOT for temporal association. The association is traditionally solved by optimizing the total cost or energy function.
For example, [2]} formulates MOT as a minimization of a continuous energy function. [1]} proposes a novel graph-based formulation that links and clusters person hypotheses over time by solving an instance of a minimum cost lifted multi-cut problem. [10]} proposes an end-to-end framework for learning parameters of min-cost flow MOT problem with quadratic trajectory interactions including suppression of overlapping tracks and contextual cues about the co-occurrence of different objects. [14]} proposes an extension to the disjoint paths problem in which additional lifted edges are introduced to provide path connectivity priors. [11]} proposes an efficient online min-cost flow tracking algorithm with bounded memory and computation.
In addition to the single-view tracking, graph based methods are also explored in multi-view tracking tasks [20]}, [21]}, [22]}, where the multi-view tracking is formulated as a graph clustering problem.
Usually, detections or tracklets are adopted as graph nodes. Then, the similarities among nodes are measured on the connected edges. For detection-based graphs, the temporal information is not well utilized and usually comes with a remarkably high dimensional affinity matrix with a heavy computational cost. The tracklet-based graph, on the other hand, is much more efficient and also incorporates global temporal information. However, the conventional graph models, based on optimization, usually suffer from empirically setting of hand-crafted features. Moreover, representative embeddings are not well-explored for the temporal association.
